Name
Abstract | ||
|---|---|---|
Parametric Markov chains (pMCs) have transitions labeled with functions over a fixed set of parameters. They are useful if the exact transition probabilities are uncertain, e.g., when checking a model for robustness. This paper presents a simple way to check whether the expected total reward until reaching a given target state is monotonic in (some of) the parameters. We exploit this monotonicity together with parameter lifting to find an e-close bound on the optimal expected total reward. Our results are also useful to automatically synthesise controllers with a fixed memory structure for partially observable Markov decision processes (POMDPs), a popular model in AI planning. We experimentally show that our approach can successfully find e-optimal controllers for optimal budget in such POMDPs. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-16336-4_6 | QUANTITATIVE EVALUATION OF SYSTEMS (QEST 2022) |
DocType | Volume | ISSN |
Conference | 13479 | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jip Spel | 1 | 0 | 0.68 |
| Svenja Stein | 2 | 0 | 0.34 |
| Joost-Pieter Katoen | 3 | 7 | 4.45 |